| from prompt_injection.evaluators.base import PromptEvaluator | |
| from sentence_transformers import SentenceTransformer | |
| import numpy as np | |
| class MiniLMEmbeddingPromptEvaluator(PromptEvaluator): | |
| def __init__(self) -> None: | |
| super().__init__() | |
| self.model=SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2') | |
| def eval_sample(self,sample): | |
| try: | |
| return self.model.encode([sample]) | |
| except Exception as err: | |
| return np.nan | |
| def get_name(self): | |
| return 'Embedding' |